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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Updated: Jun 19, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

MPOInt: An Epigenetics-Driven Workflow for Integrative Multi-Level Proteomics.

Zoe Schaefer¹, Carleigh Coffin Sokolik¹, Ivana K Parker^1,2,3

¹J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida 32610, United States.

Journal of Proteome Research

|June 18, 2026

Summary

This study introduces MPOInt, a bioinformatics pipeline for integrating multiple proteomics data types. This tool helps uncover epigenetic targets in aggressive cancers by providing clinical context to complex proteomic data.

Keywords:

BCG MCF-7 bladder cancer breast cancer disease enrichment epigenetics epiproteome histone phosphoproteomics proteomics

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Last Updated: Jun 19, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

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Published on: November 17, 2019

Area of Science:

Proteomics
Epigenetics
Bioinformatics

Background:

Proteomics offers insights into cellular regulation and epigenetic states in health and disease.
Integrating diverse proteomics data (total, phospho, epi) provides a comprehensive system view.
Current limitations include isolated data capture and complex bioinformatics for integrative analyses.

Purpose of the Study:

To develop a user-friendly pipeline for multidimensional proteomic data integration.
To enable the combination of histone proteome data with epigenetic effectors.
To identify novel therapeutic targets in aggressive cancers by adding clinical context to proteomic data.

Main Methods:

Development of the MPOInt pipeline and associated R package.
Integration of various proteomics modalities (total, phospho, epi).
Analysis combining proteomic data with pre-existing clinical data.

Main Results:

MPOInt facilitates flexible, multidimensional data analysis with a low barrier to entry.
Identification of key pathways implicated in aggressive cancer growth, including treatment-resistant breast cancer.
Generation of unique networks centered around epigenetic factors.

Conclusions:

MPOInt enhances the interpretation of complex proteomic data by adding clinical context.
The pipeline aids in discovering potential therapeutic targets for aggressive cancers.
This approach facilitates a deeper understanding of epigenetic regulation in disease states.